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Internal states such as arousal, attention and motivation modulate brain-wide neural activity, but how these processes interact with learning is not well understood. During learning, the brain modifies its neural activity to improve behavior. How do internal states affect this process? Using a brain-computer interface learning paradigm in monkeys, we identified large, abrupt fluctuations in neural population activity in motor cortex indicative of arousal-like internal state changes, which we term 'neural engagement.' In a brain-computer interface, the causal relationship between neural activity and behavior is known, allowing us to understand how neural engagement impacted behavioral performance for different task goals. We observed stereotyped changes in neural engagement that occurred regardless of how they impacted performance. This allowed us to predict how quickly different task goals were learned. These results suggest that changes in internal states, even those seemingly unrelated to goal-seeking behavior, can systematically influence how behavior improves with learning.

Motor commands for the arm and hand generally arise from the contralateral motor cortex, where most of the relevant corticospinal tract originates. However, the ipsilateral motor cortex shows activity related to arm movement despite the lack of direct connections. The extent to which the activity related to ipsilateral movement is independent from that related to contralateral movement is unclear based on conflicting conclusions in prior work. Here we investigate bilateral arm and hand movement tasks completed by two human subjects with intracortical microelectrode arrays implanted in the left hand and arm area of the motor cortex. Neural activity was recorded while they attempted to perform arm and hand movements in a virtual environment. This enabled us to quantify the strength and independence of motor cortical activity related to continuous movements of each arm. We also investigated the subjects' ability to control both arms through a brain-computer interface. Through a number of experiments, we found that ipsilateral arm movement was represented independently of, but more weakly than, contralateral arm movement. However, the representation of grasping was correlated between the two hands. This difference between hand and arm representation was unexpected and poses new questions about the different ways the motor cortex controls the hands and arms., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.)

Millions of neurons drive the activity of hundreds of muscles, meaning many different neural population activity patterns could generate the same movement. Studies have suggested that these redundant (i.e. behaviorally equivalent) activity patterns may be beneficial for neural computation. However, it is unknown what constraints may limit the selection of different redundant activity patterns. We leveraged a brain-computer interface, allowing us to define precisely which neural activity patterns were redundant. Rhesus monkeys made cursor movements by modulating neural activity in primary motor cortex. We attempted to predict the observed distribution of redundant neural activity. Principles inspired by work on muscular redundancy did not accurately predict these distributions. Surprisingly, the distributions of redundant neural activity and task-relevant activity were coupled, which enabled accurate predictions of the distributions of redundant activity. This suggests limits on the extent to which redundancy may be exploited by the brain for computation., Competing Interests: JH, MG, PL, PS, EO, KQ, SR, ET, AB, BY, SC No competing interests declared, (© 2018, Hennig et al.)

Behavior is driven by coordinated activity across a population of neurons. Learning requires the brain to change the neural population activity produced to achieve a given behavioral goal. How does population activity reorganize during learning? We studied intracortical population activity in the primary motor cortex of rhesus macaques during short-term learning in a brain-computer interface (BCI) task. In a BCI, the mapping between neural activity and behavior is exactly known, enabling us to rigorously define hypotheses about neural reorganization during learning. We found that changes in population activity followed a suboptimal neural strategy of reassociation: animals relied on a fixed repertoire of activity patterns and associated those patterns with different movements after learning. These results indicate that the activity patterns that a neural population can generate are even more constrained than previously thought and might explain why it is often difficult to quickly learn to a high level of proficiency.